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AI-Generated Information for Vascular Patients: Assessing the Standard of Procedure-Specific Information Provided by the ChatGPT AI-Language Model

Introduction Ensuring access to high-quality information is paramount to facilitating informed surgical decision-making. The use of the internet to access health-related information is increasing, along with the growing prevalence of AI language models such as ChatGPT. We aim to assess the standard...

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Autores principales: Haidar, Omar, Jaques, Alexander, McCaughran, Pierre W, Metcalfe, Matthew J
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cureus 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10691169/
https://www.ncbi.nlm.nih.gov/pubmed/38046759
http://dx.doi.org/10.7759/cureus.49764
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author Haidar, Omar
Jaques, Alexander
McCaughran, Pierre W
Metcalfe, Matthew J
author_facet Haidar, Omar
Jaques, Alexander
McCaughran, Pierre W
Metcalfe, Matthew J
author_sort Haidar, Omar
collection PubMed
description Introduction Ensuring access to high-quality information is paramount to facilitating informed surgical decision-making. The use of the internet to access health-related information is increasing, along with the growing prevalence of AI language models such as ChatGPT. We aim to assess the standard of AI-generated patient-facing information through a qualitative analysis of its readability and quality. Materials and methods We performed a retrospective qualitative analysis of information regarding three common vascular procedures: endovascular aortic repair (EVAR), endovenous laser ablation (EVLA), and femoro-popliteal bypass (FPBP). The ChatGPT responses were compared to patient information leaflets provided by the vascular charity, Circulation Foundation UK. Readability was assessed using four readability scores: the Flesch-Kincaid reading ease (FKRE) score, the Flesch-Kincaid grade level (FKGL), the Gunning fog score (GFS), and the simple measure of gobbledygook (SMOG) index. Quality was assessed using the DISCERN tool by two independent assessors. Results The mean FKRE score was 33.3, compared to 59.1 for the information provided by the Circulation Foundation (SD=14.5, p=0.025) indicating poor readability of AI-generated information. The FFKGL indicated that the expected grade of students likely to read and understand ChatGPT responses was consistently higher than compared to information leaflets at 12.7 vs. 9.4 (SD=1.9, p=0.002). Two metrics measure readability in terms of the number of years of education required to understand a piece of writing: the GFS and SMOG. Both scores indicated that AI-generated answers were less accessible. The GFS for ChatGPT-provided information was 16.7 years versus 12.8 years for the leaflets (SD=2.2, p=0.002) and the SMOG index scores were 12.2 and 9.4 years for ChatGPT and the patient information leaflets, respectively (SD=1.7, p=0.001). The DISCERN scores were consistently higher in human-generated patient information leaflets compared to AI-generated information across all procedures; the mean score for the information provided by ChatGPT was 50.3 vs. 56.0 for the Circulation Foundation information leaflets (SD=3.38, p<0.001). Conclusion We concluded that AI-generated information about vascular surgical procedures is currently poor in both the readability of text and the quality of information. Patients should be directed to reputable, human-generated information sources from trusted professional bodies to supplement direct education from the clinician during the pre-procedure consultation process.
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spelling pubmed-106911692023-12-02 AI-Generated Information for Vascular Patients: Assessing the Standard of Procedure-Specific Information Provided by the ChatGPT AI-Language Model Haidar, Omar Jaques, Alexander McCaughran, Pierre W Metcalfe, Matthew J Cureus Cardiac/Thoracic/Vascular Surgery Introduction Ensuring access to high-quality information is paramount to facilitating informed surgical decision-making. The use of the internet to access health-related information is increasing, along with the growing prevalence of AI language models such as ChatGPT. We aim to assess the standard of AI-generated patient-facing information through a qualitative analysis of its readability and quality. Materials and methods We performed a retrospective qualitative analysis of information regarding three common vascular procedures: endovascular aortic repair (EVAR), endovenous laser ablation (EVLA), and femoro-popliteal bypass (FPBP). The ChatGPT responses were compared to patient information leaflets provided by the vascular charity, Circulation Foundation UK. Readability was assessed using four readability scores: the Flesch-Kincaid reading ease (FKRE) score, the Flesch-Kincaid grade level (FKGL), the Gunning fog score (GFS), and the simple measure of gobbledygook (SMOG) index. Quality was assessed using the DISCERN tool by two independent assessors. Results The mean FKRE score was 33.3, compared to 59.1 for the information provided by the Circulation Foundation (SD=14.5, p=0.025) indicating poor readability of AI-generated information. The FFKGL indicated that the expected grade of students likely to read and understand ChatGPT responses was consistently higher than compared to information leaflets at 12.7 vs. 9.4 (SD=1.9, p=0.002). Two metrics measure readability in terms of the number of years of education required to understand a piece of writing: the GFS and SMOG. Both scores indicated that AI-generated answers were less accessible. The GFS for ChatGPT-provided information was 16.7 years versus 12.8 years for the leaflets (SD=2.2, p=0.002) and the SMOG index scores were 12.2 and 9.4 years for ChatGPT and the patient information leaflets, respectively (SD=1.7, p=0.001). The DISCERN scores were consistently higher in human-generated patient information leaflets compared to AI-generated information across all procedures; the mean score for the information provided by ChatGPT was 50.3 vs. 56.0 for the Circulation Foundation information leaflets (SD=3.38, p<0.001). Conclusion We concluded that AI-generated information about vascular surgical procedures is currently poor in both the readability of text and the quality of information. Patients should be directed to reputable, human-generated information sources from trusted professional bodies to supplement direct education from the clinician during the pre-procedure consultation process. Cureus 2023-11-30 /pmc/articles/PMC10691169/ /pubmed/38046759 http://dx.doi.org/10.7759/cureus.49764 Text en Copyright © 2023, Haidar et al. https://creativecommons.org/licenses/by/3.0/This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Cardiac/Thoracic/Vascular Surgery
Haidar, Omar
Jaques, Alexander
McCaughran, Pierre W
Metcalfe, Matthew J
AI-Generated Information for Vascular Patients: Assessing the Standard of Procedure-Specific Information Provided by the ChatGPT AI-Language Model
title AI-Generated Information for Vascular Patients: Assessing the Standard of Procedure-Specific Information Provided by the ChatGPT AI-Language Model
title_full AI-Generated Information for Vascular Patients: Assessing the Standard of Procedure-Specific Information Provided by the ChatGPT AI-Language Model
title_fullStr AI-Generated Information for Vascular Patients: Assessing the Standard of Procedure-Specific Information Provided by the ChatGPT AI-Language Model
title_full_unstemmed AI-Generated Information for Vascular Patients: Assessing the Standard of Procedure-Specific Information Provided by the ChatGPT AI-Language Model
title_short AI-Generated Information for Vascular Patients: Assessing the Standard of Procedure-Specific Information Provided by the ChatGPT AI-Language Model
title_sort ai-generated information for vascular patients: assessing the standard of procedure-specific information provided by the chatgpt ai-language model
topic Cardiac/Thoracic/Vascular Surgery
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10691169/
https://www.ncbi.nlm.nih.gov/pubmed/38046759
http://dx.doi.org/10.7759/cureus.49764
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